ABSTRACT
This paper studies social interaction models with both in-group and out-group effects. The in-group effect follows the standard setup in the literature, while the out-group effect is introduced by assuming the economic outcome also depends on its out-group average value. We present a network game with limited information of outside groups that rationalizes the econometric model. We show that both effects are identified under a set of mild regularity conditions. We propose to estimate the model using the two-stage least squares (2SLS) method and establish the asymptotic normality of the estimators. The finite sample performance of the estimators is investigated through Monte Carlo simulations.
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Supplementary material
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Notes
1 A adjacency matrix
is defined as follows. If
and
are connected, then
, otherwise
.
2 It is a convention in the literature of social interaction models to assume that the individual characteristics are nonstochastic; seeLee (Citation2004) andLee (Citation2007), among many others.
3 In the example of student’s academic achievement, will be student
‘s GPA,
will be a vector of exogenous variables that may affect student’s academic achievement, such as age and parents’ education.
is the average GPA of student
‘s connected friends, and
is the average GPA outside student
‘s classroom.
4 The identification results can be derived similarly for models with fixed effects but estimation procedure would be much more complicated; see Lee (Citation2007) for more details. We leave EquationEquation (1)(1)
(1) with group-specific fixed effects as a future research direction.
5 The only reference we find is Tchuente (Citation2019) who considers the identification and estimation of social interaction effect in the classic social interaction model, i.e. there only exists the in-group effect.